Oluwatoyin Ishola1* , Mariam Masud Oniye2, Ugochukwu Onyemaobi3 , Ayobami Shadam 4
¹Department of Science Laboratory Technology, Kwara State Polytechnic, Ilorin, Nigeria
²Department of Biochemistry, Kaduna State University, Kaduna, Nigeria
3 Department of Biochemistry, Kaduna State University, Kaduna, Nigeria
4 Department of Chemistry, Faculty of Science, University of Nivi
Publication History
Received 20.02.2025
Accepted 25.05.2025
Published online 16.06.2025
Cite as:
Ishola, O., Masud, M. O., Onyemaobi, U., & Shadam, A. (2025). Chemometric Analysis of heavy metal contamination in water sources: A review on risk Assessment approach. International Journal of Scholarly Resources, 18(1), 50–61.
Abstract
Heavy metal contamination in water sources represents a critical environmental and public health concern worldwide. This review synthesizes recent advances in chemometric techniques for analyzing heavy metal contamination patterns and their integration with risk assessment frameworks. A comprehensive literature search was conducted focusing on studies published between 2020-2025, revealing significant developments in multivariate statistical analysis, machine learning approaches, and probabilistic risk assessment methods. The analysis of 30 recent studies demonstrates that chemometric techniques, particularly Principal Component Analysis (PCA), cluster analysis, and Positive Matrix Factorization (PMF), are increasingly being combined with Monte Carlo simulation and Geographic Information Systems (GIS) for comprehensive risk assessment. Key findings indicate that multivariate statistical approaches effectively identify contamination sources, with 70-85% variance explanation in most studies. Risk assessment integration shows that Monte Carlo simulation provides robust probabilistic estimates, with children showing 2-3 times higher health risks than adults. The review identifies emerging trends including artificial intelligence applications, real-time monitoring systems, and integrated decision support platforms. Challenges include data quality standardization, model interpretability, and uncertainty quantification. Future research should focus on developing standardized protocols, enhancing model interpretability, and creating user-friendly decision support tools for environmental managers and policymakers.
Keywords:
Heavy metals, Water contamination, Chemometrics, Risk assessment, Multivariate analysis, Monte Carlo simulation, Principal Component Analysis, Environmental monitoring, Public health, Water quality
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